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Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception
Vision-language models (VLMs) have shown powerful capabilities in visual question answering and reasoning tasks by combining visual representations with the abstract skill set large language models (LLMs) learn during pretraining.
Assessing Trust in Construction AI-Powered Collaborative Robots using Structural Equation Modeling
This study aimed to investigate the key technical and psychological factors that impact the architecture, engineering, and construction (AEC) professionals' trust in collaborative robots (cobots) powered by artificial intelligence (AI).
Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis
Construction technology researchers and forward-thinking companies are experimenting with collaborative robots (aka cobots), powered by artificial intelligence (AI), to explore various automation scenarios as part of the digital transformation of the industry.
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning
Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers' activities.
Trust in AI and Implications for the AEC Research: A Literature Analysis
Engendering trust in technically acceptable and psychologically embraceable systems requires domain-specific research to capture unique characteristics of the field of application.
Automated Workers Ergonomic Risk Assessment in Manual Material Handling using sEMG Wearable Sensors and Machine Learning
This paper evaluates the ability of surface electromyogram (EMG)-based systems together with machine learning algorithms to automatically detect body movements that may harm muscles in material handling.
Trustworthy AI and Robotics and the Implications for the AEC Industry: A Systematic Literature Review and Future Potentials
As the applications of artificial intelligence (AI) and robotics emerge and with their ever-growing socio-economic influence in various fields of research and practice, there is an imminent need to study trust in such systems.
Automated Estimation of Construction Equipment Emission using Inertial Sensors and Machine Learning Models
A portable emission measurement system (PEMS) was employed along with the inertial sensors to record data including the amount of CO, NOX, CO2, SO2, and CH4 pollutions emitted by the equipment.
